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        <title>Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings</title>
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    <name type="personal">
        <namePart type="given">Matthew</namePart>
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    <name type="personal">
        <namePart type="given">Laetitia</namePart>
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    <name type="personal">
        <namePart type="given">Douwe</namePart>
        <namePart type="family">Kiela</namePart>
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    <name type="personal">
        <namePart type="given">Maximilian</namePart>
        <namePart type="family">Nickel</namePart>
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            <namePart type="given">David</namePart>
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            <namePart type="given">Lluís</namePart>
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    <abstract>We consider the task of inferring “is-a” relationships from large text corpora. For this purpose, we propose a new method combining hyperbolic embeddings and Hearst patterns. This approach allows us to set appropriate constraints for inferring concept hierarchies from distributional contexts while also being able to predict missing “is-a”-relationships and to correct wrong extractions. Moreover – and in contrast with other methods – the hierarchical nature of hyperbolic space allows us to learn highly efficient representations and to improve the taxonomic consistency of the inferred hierarchies. Experimentally, we show that our approach achieves state-of-the-art performance on several commonly-used benchmarks.</abstract>
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%0 Conference Proceedings
%T Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings
%A Le, Matthew
%A Roller, Stephen
%A Papaxanthos, Laetitia
%A Kiela, Douwe
%A Nickel, Maximilian
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F le-etal-2019-inferring
%X We consider the task of inferring “is-a” relationships from large text corpora. For this purpose, we propose a new method combining hyperbolic embeddings and Hearst patterns. This approach allows us to set appropriate constraints for inferring concept hierarchies from distributional contexts while also being able to predict missing “is-a”-relationships and to correct wrong extractions. Moreover – and in contrast with other methods – the hierarchical nature of hyperbolic space allows us to learn highly efficient representations and to improve the taxonomic consistency of the inferred hierarchies. Experimentally, we show that our approach achieves state-of-the-art performance on several commonly-used benchmarks.
%R 10.18653/v1/P19-1313
%U https://aclanthology.org/P19-1313
%U https://doi.org/10.18653/v1/P19-1313
%P 3231-3241

Markdown (Informal)

[Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings](https://aclanthology.org/P19-1313) (Le et al., ACL 2019)

• Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings (Le et al., ACL 2019)

ACL

• Matthew Le, Stephen Roller, Laetitia Papaxanthos, Douwe Kiela, and Maximilian Nickel. 2019. Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3231–3241, Florence, Italy. Association for Computational Linguistics.